k-means - CRC Batch 2 Set 3
Sophie Herbst
Analysis date: 2023-08-11
Depends on
CRC_Xenografts_Batch2_DataProcessing Script
load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")
set.seed(2023)Setup
Load libraries
Functions
General
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorssource("CRC_Xenografts_Batch2_Functions.R")k-means Peptide - pY
Format data
mat_kmean_pY <-
pY_Set3_form %>%
mutate(peptide = paste0(HGNC_Symbol, "_", Annotated_Sequence) ) %>%
column_to_rownames("peptide") %>%
select(all_of(contains("log2FC"))) %>%
as.matrix()
kmeans_tb_pY <-
pY_Set3_form %>%
select(HGNC_Symbol, Annotated_Sequence, all_of(contains("log2FC"))) %>%
pivot_longer(contains("log2FC"), names_to = "sample", values_to = "log2FC") %>%
separate(col = sample, sep = "_",
into = c("remove", "xenograft", "treatment",
"timepoint", "replicate", "set" ), remove = F ) %>%
mutate(peptide = paste0(HGNC_Symbol, "_", Annotated_Sequence) )Find optimal number of clusters
KMeans_Find_Nr_Clusters_elbow(mat_kmean_pY, c_max = 30)
Perform k-means
pY_kmeans = kmeans(mat_kmean_pY, centers = 6, nstart = 200, iter.max = 10)
cluster_df_pY <- tibble("cluster" = pY_kmeans$cluster)
cluster_df_pY$peptide <- names(pY_kmeans$cluster)
kmeans_tb_pY <- left_join(kmeans_tb_pY, cluster_df_pY)## Joining with `by = join_by(peptide)`Plot profiles
kmeans_tb_pY %>%
ggplot(aes(sample, log2FC, group = Annotated_Sequence)) +
geom_line(alpha = 0.2) +
geom_point(aes(color = treatment), size = 0.2) +
facet_wrap(~cluster) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = PGPalette[c(1,2,4,5)])
Print clusters
DT::datatable(filter="top", kmeans_tb_pY %>%
select(HGNC_Symbol, Annotated_Sequence, cluster) %>%
unique)StringDB clusters
message("Cluster 3")## Cluster 3Plot_StringDB(kmeans_tb_pY %>%
select(HGNC_Symbol, Annotated_Sequence, cluster) %>%
filter(cluster == 3) %>%
select(HGNC_Symbol) %>% unique())
k-means Peptide - pST
Format data
mat_kmean_pST <-
pST_Set3_form %>%
mutate(peptide = paste0(HGNC_Symbol, "_", Annotated_Sequence) ) %>%
column_to_rownames("peptide") %>%
select(all_of(contains("log2FC"))) %>%
as.matrix()
kmeans_tb_pST <-
pST_Set3_form %>%
select(HGNC_Symbol, Annotated_Sequence, all_of(contains("log2FC"))) %>%
pivot_longer(contains("log2FC"), names_to = "sample", values_to = "log2FC") %>%
separate(col = sample, sep = "_",
into = c("remove", "xenograft", "treatment",
"timepoint", "replicate", "set" ), remove = F ) %>%
mutate(peptide = paste0(HGNC_Symbol, "_", Annotated_Sequence) )Find optimal number of clusters
KMeans_Find_Nr_Clusters_elbow(mat_kmean_pST, c_max = 30)
Perform k-means
pST_kmeans = kmeans(mat_kmean_pST, centers = 3, nstart = 200, iter.max = 10)
cluster_df_pST <- tibble("cluster" = pST_kmeans$cluster)
cluster_df_pST$peptide <- names(pST_kmeans$cluster)
kmeans_tb_pST <-
left_join(kmeans_tb_pST, cluster_df_pST)## Joining with `by = join_by(peptide)`Plot profiles
kmeans_tb_pST %>%
ggplot(aes(sample, log2FC, group = Annotated_Sequence)) +
geom_line(alpha = 0.2) +
geom_point(aes(color = treatment), size = 0.2) +
facet_wrap(~cluster, ncol = 3) +
theme_bw() +
theme(axis.text.x = element_text(angle = 90)) +
scale_color_manual(values = PGPalette[c(1,2,4,5)])
Print clusters
DT::datatable( filter="top", kmeans_tb_pST %>%
select(HGNC_Symbol, Annotated_Sequence, cluster) %>%
unique)Session Info
sessionInfo()## R version 4.2.3 (2023-03-15)
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